PPAR signaling pathway may be an important predictor of breast cancer response to neoadjuvant chemotherapy

Chen, Y. Z.; Xue, Jian; Chen, C. M.; Yang, Benlong; Xu, Qinghua; Wu, Fei; Liu, F.; Ye, X.; Xia, Meng; Liu, G. Y.; Shen, Zhenzhou; Shao, Zhi Ming; Wu, Jiong

doi:10.1007/s00280-012-1949-0

Cited by 61 publications

(51 citation statements)

References 19 publications

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“…“PPAR signaling pathway” is most differentially regulated pathway gene set with a strong network rewiring. The pathway has been implicated in fatty acid oxidation and its activators have been proposed for treatment of cancer and other metabolic diseases3031. One of the major regulators in the pathway, PPARA gene, is a nuclear TF superfamily protein, i.e.…”

Section: Resultsmentioning

confidence: 99%

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Kaushik

Ali

Gupta

2017

Sci Rep

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Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA.

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Section: Resultsmentioning

confidence: 99%

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Kaushik

Ali

Gupta

2017

Sci Rep

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“…Signalling pathways were ranked by their average AUC values over independent data sets examined. Pathways like ERBB, PTEN, BRCA1, PPAR, TGF-beta and RAS, previously reported to trigger paclitaxel treatment response, can be found in these lists3031323334. Although the iPANDA-generated pathway marker lists obtained within data on the same cancer type have noticeable intersection, the lists of markers differ significantly between cancer types.…”

Section: Resultsmentioning

confidence: 98%

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

et al. 2016

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Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

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“…For HRNM_T versus LRYM_T, DEGs were up-regulated mainly in the PPAR signaling pathway and the adipocytokine signaling pathway. Peroxisome proliferator-activated receptor (PPAR) signaling pathways are nuclear hormone receptors that are activated by fatty acids and their derivatives and may be an important predictor of breast cancer response to neoadjuvant chemotherapy [22]. Expression of the nuclear receptor peroxisome proliferator activated receptor delta (PPARdelta) in breast cancer cells is negatively associated with patient survival [23].…”

Section: Discussionmentioning

confidence: 99%

RNA-Sequence Analysis Reveals Differentially Expressed Genes (DEGs) in Patients Exhibiting Different Risks of Tumor Metastasis

Dong

Long

et al. 2017

Med Sci Monit

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BackgroundBreast cancer is one of the most common malignancies in women. In a previous study, we found that for two patients who had a high risk of lymphatic metastasis, lymphatic metastasis did not occur; whereas, for two patients who had a low risk of lymphatic metastasis, lymphatic metastasis did occur.Material/MethodsWe analyzed the differential gene expressions of these four patients by RNA-sequence. The data (HRNM_T versus HRNM_N, LRYM_T versus LRYM_N, and HRNM_T versus LRYM_T) was then processed using differentially expressed genes (DEGs) analysis, functional analysis for DEGs, and PPI network construct.ResultsFor HRNM_T versus HRNM_N, there were 224 DEGs. There were 504 DEGs for LRYM_T versus LRYM_N, and 88 DEGs for LRYM_T versus LRYM_N. For HRNM_T versus HRNM_N, DEGs were up-regulated mainly in the cell cycle, the IL-17 signaling pathway, and the progesterone-mediated oocyte maturation; DEGs were down-regulated mainly in the IL-17 signaling pathway. For LRYM_T versus LRYM_N, DEGs were up-regulated mainly in protein digestion and absorption, and cytokine-cytokine receptor interaction; DEGs were down-regulated mainly in ECM-receptor interaction. For HRNM_T versus LRYM_T, DEGs were up-regulated mainly in the PPAR signaling pathway; DEGs were downregulated mainly in the adipocytokine signaling pathway. The DEGs were screened to construct PPI networks.ConclusionsThe GO and KEGG functional enrichments of HRNM_T versus HRNM_N, and LRYM_T versus LRYM_N were consistent with earlier studies. For HRNM_T versus LRYM_T, DEGs were up-regulated mainly in PPAR signaling; DEGs were down-regulated mainly in the adipocytokine pathway.

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PPAR signaling pathway may be an important predictor of breast cancer response to neoadjuvant chemotherapy

Abstract: In the present study, we identified a number of gene categories pertinent to the therapeutic response. We believe that the PPAR pathway may be an important predictor of genes that are involved in the chemotherapy response.

Cited by 61 publications

References 19 publications

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

RNA-Sequence Analysis Reveals Differentially Expressed Genes (DEGs) in Patients Exhibiting Different Risks of Tumor Metastasis

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